Overview

Dataset statistics

Number of variables16
Number of observations1074606
Missing cells186888
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory131.2 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 43376 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 46722 (4.3%) missing values Missing
jerseyNumber has 46722 (4.3%) missing values Missing
o has 46722 (4.3%) missing values Missing
dir has 46722 (4.3%) missing values Missing
s has 64921 (6.0%) zeros Zeros
a has 60354 (5.6%) zeros Zeros
dis has 65343 (6.1%) zeros Zeros

Reproduction

Analysis started2022-11-02 14:56:39.168171
Analysis finished2022-11-02 14:58:15.968339
Duration1 minute and 36.8 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021099932
Minimum2021093000
Maximum2021100400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:16.013964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021093000
5-th percentile2021093000
Q12021100303
median2021100307
Q32021100311
95-th percentile2021100400
Maximum2021100400
Range7400
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1625.232664
Coefficient of variation (CV)8.041327587 × 10-7
Kurtosis14.2437537
Mean2021099932
Median Absolute Deviation (MAD)4
Skewness-4.029836525
Sum2.171886113 × 1015
Variance2641381.212
MonotonicityIncreasing
2022-11-02T11:58:16.106725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
202110030783605
 
7.8%
202110030078407
 
7.3%
202110031178177
 
7.3%
202110031376613
 
7.1%
202110030973048
 
6.8%
202110030572657
 
6.8%
202110030869391
 
6.5%
202110040068103
 
6.3%
202110030466378
 
6.2%
202110031062445
 
5.8%
Other values (6)345782
32.2%
ValueCountFrequency (%)
202109300055982
5.2%
202110030078407
7.3%
202110030149036
4.6%
202110030260007
5.6%
202110030361410
5.7%
202110030466378
6.2%
202110030572657
6.8%
202110030659616
5.5%
202110030783605
7.8%
202110030869391
6.5%
ValueCountFrequency (%)
202110040068103
6.3%
202110031376613
7.1%
202110031259731
5.6%
202110031178177
7.3%
202110031062445
5.8%
202110030973048
6.8%
202110030869391
6.5%
202110030783605
7.8%
202110030659616
5.5%
202110030572657
6.8%

playId
Real number (ℝ≥0)

Distinct987
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2225.051625
Minimum55
Maximum5153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:16.224996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile248
Q11172
median2252
Q33312
95-th percentile4114
Maximum5153
Range5098
Interquartile range (IQR)2140

Descriptive statistics

Standard deviation1244.912922
Coefficient of variation (CV)0.5594984443
Kurtosis-1.102648498
Mean2225.051625
Median Absolute Deviation (MAD)1074
Skewness0.01012728136
Sum2391053826
Variance1549808.184
MonotonicityNot monotonic
2022-11-02T11:58:16.357474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25054922
 
0.5%
1983611
 
0.3%
41383381
 
0.3%
32063266
 
0.3%
33312921
 
0.3%
15222921
 
0.3%
3172852
 
0.3%
23682806
 
0.3%
20232783
 
0.3%
762737
 
0.3%
Other values (977)1042406
97.0%
ValueCountFrequency (%)
551334
0.1%
56667
 
0.1%
591449
0.1%
762737
0.3%
78897
 
0.1%
80667
 
0.1%
81690
 
0.1%
831035
 
0.1%
971449
0.1%
104943
 
0.1%
ValueCountFrequency (%)
51531265
0.1%
5108805
0.1%
5073736
0.1%
5051920
0.1%
5010828
0.1%
4953713
0.1%
4929851
0.1%
48431035
0.1%
48241058
0.1%
4668713
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1174
Distinct (%)0.1%
Missing46722
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45665.46062
Minimum25511
Maximum54006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:16.488520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile37130
Q142431
median45215
Q348089
95-th percentile53476
Maximum54006
Range28495
Interquartile range (IQR)5658

Descriptive statistics

Standard deviation5042.442404
Coefficient of variation (CV)0.110421363
Kurtosis-0.01150870586
Mean45665.46062
Median Absolute Deviation (MAD)2788
Skewness-0.1946866213
Sum4.693879633 × 1010
Variance25426225.4
MonotonicityNot monotonic
2022-11-02T11:58:16.612961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412432211
 
0.2%
385382211
 
0.2%
478652211
 
0.2%
401242211
 
0.2%
525662211
 
0.2%
478812104
 
0.2%
412372022
 
0.2%
386292010
 
0.2%
524612002
 
0.2%
525532002
 
0.2%
Other values (1164)1006689
93.7%
(Missing)46722
 
4.3%
ValueCountFrequency (%)
255111707
0.2%
289631253
0.1%
29550776
0.1%
298511344
0.1%
30842431
 
< 0.1%
308691623
0.2%
330841769
0.2%
33107925
0.1%
33130350
 
< 0.1%
331311296
0.1%
ValueCountFrequency (%)
5400682
 
< 0.1%
5399976
 
< 0.1%
53978427
< 0.1%
53960141
 
< 0.1%
53957702
0.1%
53953287
< 0.1%
53946194
 
< 0.1%
53900182
 
< 0.1%
53711118
 
< 0.1%
53679127
 
< 0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct119
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.20987971
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:16.773653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q332
95-th percentile51
Maximum119
Range118
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.50913708
Coefficient of variation (CV)0.6682127296
Kurtosis1.527209904
Mean23.20987971
Median Absolute Deviation (MAD)11
Skewness0.9521722351
Sum24941476
Variance240.5333329
MonotonicityNot monotonic
2022-11-02T11:58:16.909951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125599
 
2.4%
1325599
 
2.4%
2225599
 
2.4%
2125599
 
2.4%
2025599
 
2.4%
1925599
 
2.4%
1825599
 
2.4%
1725599
 
2.4%
1625599
 
2.4%
225599
 
2.4%
Other values (109)818616
76.2%
ValueCountFrequency (%)
125599
2.4%
225599
2.4%
325599
2.4%
425599
2.4%
525599
2.4%
625599
2.4%
725599
2.4%
825599
2.4%
925599
2.4%
1025599
2.4%
ValueCountFrequency (%)
11923
< 0.1%
11823
< 0.1%
11746
< 0.1%
11646
< 0.1%
11546
< 0.1%
11446
< 0.1%
11346
< 0.1%
11246
< 0.1%
11146
< 0.1%
11046
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct43376
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
2021-10-03T19:38:04.100
 
92
2021-10-03T19:38:04.000
 
92
2021-10-03T19:38:03.900
 
92
2021-10-03T19:38:03.800
 
92
2021-10-03T19:38:03.700
 
92
Other values (43371)
1074146 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters24715938
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-01T00:29:42.800
2nd row2021-10-01T00:29:42.900
3rd row2021-10-01T00:29:43.000
4th row2021-10-01T00:29:43.100
5th row2021-10-01T00:29:43.200

Common Values

ValueCountFrequency (%)
2021-10-03T19:38:04.10092
 
< 0.1%
2021-10-03T19:38:04.00092
 
< 0.1%
2021-10-03T19:38:03.90092
 
< 0.1%
2021-10-03T19:38:03.80092
 
< 0.1%
2021-10-03T19:38:03.70092
 
< 0.1%
2021-10-03T19:38:03.60092
 
< 0.1%
2021-10-03T19:38:03.50092
 
< 0.1%
2021-10-03T19:38:04.20092
 
< 0.1%
2021-10-03T19:26:13.10069
 
< 0.1%
2021-10-03T19:26:12.60069
 
< 0.1%
Other values (43366)1073732
99.9%

Length

2022-11-02T11:58:17.028799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-10-03t19:38:04.10092
 
< 0.1%
2021-10-03t19:38:03.90092
 
< 0.1%
2021-10-03t19:38:03.80092
 
< 0.1%
2021-10-03t19:38:03.70092
 
< 0.1%
2021-10-03t19:38:03.60092
 
< 0.1%
2021-10-03t19:38:03.50092
 
< 0.1%
2021-10-03t19:38:04.20092
 
< 0.1%
2021-10-03t19:38:04.00092
 
< 0.1%
2021-10-03t19:48:30.10069
 
< 0.1%
2021-10-03t19:24:19.00069
 
< 0.1%
Other values (43366)1073732
99.9%

Most occurring characters

ValueCountFrequency (%)
06423854
26.0%
13564287
14.4%
23293209
13.3%
-2149212
 
8.7%
:2149212
 
8.7%
31591876
 
6.4%
T1074606
 
4.3%
.1074606
 
4.3%
4793178
 
3.2%
5750582
 
3.0%
Other values (4)1851316
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18268302
73.9%
Other Punctuation3223818
 
13.0%
Dash Punctuation2149212
 
8.7%
Uppercase Letter1074606
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06423854
35.2%
13564287
19.5%
23293209
18.0%
31591876
 
8.7%
4793178
 
4.3%
5750582
 
4.1%
7517500
 
2.8%
9507357
 
2.8%
8490429
 
2.7%
6336030
 
1.8%
Other Punctuation
ValueCountFrequency (%)
:2149212
66.7%
.1074606
33.3%
Dash Punctuation
ValueCountFrequency (%)
-2149212
100.0%
Uppercase Letter
ValueCountFrequency (%)
T1074606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common23641332
95.7%
Latin1074606
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
06423854
27.2%
13564287
15.1%
23293209
13.9%
-2149212
 
9.1%
:2149212
 
9.1%
31591876
 
6.7%
.1074606
 
4.5%
4793178
 
3.4%
5750582
 
3.2%
7517500
 
2.2%
Other values (3)1333816
 
5.6%
Latin
ValueCountFrequency (%)
T1074606
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII24715938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06423854
26.0%
13564287
14.4%
23293209
13.3%
-2149212
 
8.7%
:2149212
 
8.7%
31591876
 
6.4%
T1074606
 
4.3%
.1074606
 
4.3%
4793178
 
3.2%
5750582
 
3.0%
Other values (4)1851316
 
7.5%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)< 0.1%
Missing46722
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean50.08508548
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:17.137247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median52
Q376
95-th percentile96
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.9047556
Coefficient of variation (CV)0.5970790569
Kurtosis-1.324927188
Mean50.08508548
Median Absolute Deviation (MAD)27
Skewness0.01619268276
Sum51481658
Variance894.2944076
MonotonicityNot monotonic
2022-11-02T11:58:17.266515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2322465
 
2.1%
219372
 
1.8%
2619147
 
1.8%
7618699
 
1.7%
9717630
 
1.6%
1117378
 
1.6%
7716813
 
1.6%
9916459
 
1.5%
2216450
 
1.5%
2116367
 
1.5%
Other values (89)847104
78.8%
(Missing)46722
 
4.3%
ValueCountFrequency (%)
111990
1.1%
219372
1.8%
36908
 
0.6%
49871
0.9%
56417
 
0.6%
69931
0.9%
78813
0.8%
89695
0.9%
98779
0.8%
1011103
1.0%
ValueCountFrequency (%)
9916459
1.5%
9815458
1.4%
9717630
1.6%
969644
0.9%
9510474
1.0%
9415613
1.5%
9310263
1.0%
925872
 
0.5%
9113004
1.2%
9014454
1.3%

team
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
football
 
46722
NYJ
 
39985
TEN
 
39985
ATL
 
37499
WAS
 
37499
Other values (28)
872916 

Length

Max length8
Median length3
Mean length2.974586034
Min length2

Characters and Unicode

Total characters3196508
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJAX
2nd rowJAX
3rd rowJAX
4th rowJAX
5th rowJAX

Common Values

ValueCountFrequency (%)
football46722
 
4.3%
NYJ39985
 
3.7%
TEN39985
 
3.7%
ATL37499
 
3.5%
WAS37499
 
3.5%
DEN37389
 
3.5%
BAL37389
 
3.5%
NE36641
 
3.4%
TB36641
 
3.4%
ARI34936
 
3.3%
Other values (23)689920
64.2%

Length

2022-11-02T11:58:17.385160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football46722
 
4.3%
nyj39985
 
3.7%
ten39985
 
3.7%
atl37499
 
3.5%
was37499
 
3.5%
den37389
 
3.5%
bal37389
 
3.5%
ne36641
 
3.4%
tb36641
 
3.4%
ari34936
 
3.3%
Other values (23)689920
64.2%

Most occurring characters

ValueCountFrequency (%)
A361955
 
11.3%
N304293
 
9.5%
I250404
 
7.8%
L239085
 
7.5%
E207328
 
6.5%
C185350
 
5.8%
T171391
 
5.4%
D127204
 
4.0%
B126049
 
3.9%
S97229
 
3.0%
Other values (20)1126220
35.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2822732
88.3%
Lowercase Letter373776
 
11.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A361955
12.8%
N304293
10.8%
I250404
 
8.9%
L239085
 
8.5%
E207328
 
7.3%
C185350
 
6.6%
T171391
 
6.1%
D127204
 
4.5%
B126049
 
4.5%
S97229
 
3.4%
Other values (14)752444
26.7%
Lowercase Letter
ValueCountFrequency (%)
l93444
25.0%
o93444
25.0%
a46722
12.5%
b46722
12.5%
t46722
12.5%
f46722
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3196508
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A361955
 
11.3%
N304293
 
9.5%
I250404
 
7.8%
L239085
 
7.5%
E207328
 
6.5%
C185350
 
5.8%
T171391
 
5.4%
D127204
 
4.0%
B126049
 
3.9%
S97229
 
3.0%
Other values (20)1126220
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3196508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A361955
 
11.3%
N304293
 
9.5%
I250404
 
7.8%
L239085
 
7.5%
E207328
 
6.5%
C185350
 
5.8%
T171391
 
5.4%
D127204
 
4.0%
B126049
 
3.9%
S97229
 
3.0%
Other values (20)1126220
35.2%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
left
553725 
right
520881 

Length

Max length5
Median length4
Mean length4.48471812
Min length4

Characters and Unicode

Total characters4819305
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
left553725
51.5%
right520881
48.5%

Length

2022-11-02T11:58:17.479441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:58:17.574957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
left553725
51.5%
right520881
48.5%

Most occurring characters

ValueCountFrequency (%)
t1074606
22.3%
l553725
11.5%
e553725
11.5%
f553725
11.5%
r520881
10.8%
i520881
10.8%
g520881
10.8%
h520881
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4819305
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1074606
22.3%
l553725
11.5%
e553725
11.5%
f553725
11.5%
r520881
10.8%
i520881
10.8%
g520881
10.8%
h520881
10.8%

Most occurring scripts

ValueCountFrequency (%)
Latin4819305
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1074606
22.3%
l553725
11.5%
e553725
11.5%
f553725
11.5%
r520881
10.8%
i520881
10.8%
g520881
10.8%
h520881
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4819305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1074606
22.3%
l553725
11.5%
e553725
11.5%
f553725
11.5%
r520881
10.8%
i520881
10.8%
g520881
10.8%
h520881
10.8%

x
Real number (ℝ)

Distinct11870
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.16086157
Minimum-1.9
Maximum120
Zeros1
Zeros (%)< 0.1%
Negative30
Negative (%)< 0.1%
Memory size8.2 MiB
2022-11-02T11:58:17.670915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.9
5-th percentile17.24
Q136.87
median55.23
Q375.08
95-th percentile96.81
Maximum120
Range121.9
Interquartile range (IQR)38.21

Descriptive statistics

Standard deviation24.51044829
Coefficient of variation (CV)0.4364329109
Kurtosis-0.8003671711
Mean56.16086157
Median Absolute Deviation (MAD)19.11
Skewness0.109251926
Sum60350798.81
Variance600.7620755
MonotonicityNot monotonic
2022-11-02T11:58:17.815073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.49205
 
< 0.1%
48.5202
 
< 0.1%
35.28199
 
< 0.1%
54.66197
 
< 0.1%
45.57197
 
< 0.1%
52.34197
 
< 0.1%
45.95195
 
< 0.1%
54.68193
 
< 0.1%
34.03193
 
< 0.1%
52.17193
 
< 0.1%
Other values (11860)1072635
99.8%
ValueCountFrequency (%)
-1.91
< 0.1%
-1.671
< 0.1%
-1.431
< 0.1%
-1.171
< 0.1%
-0.881
< 0.1%
-0.751
< 0.1%
-0.741
< 0.1%
-0.711
< 0.1%
-0.681
< 0.1%
-0.641
< 0.1%
ValueCountFrequency (%)
1202
< 0.1%
119.991
< 0.1%
119.981
< 0.1%
119.961
< 0.1%
119.951
< 0.1%
119.911
< 0.1%
119.91
< 0.1%
119.861
< 0.1%
119.831
< 0.1%
119.811
< 0.1%

y
Real number (ℝ)

Distinct5391
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.83270419
Minimum-1.76
Maximum54.88
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)< 0.1%
Memory size8.2 MiB
2022-11-02T11:58:17.960979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.76
5-th percentile11.83
Q122.09
median26.87
Q331.56
95-th percentile41.64
Maximum54.88
Range56.64
Interquartile range (IQR)9.47

Descriptive statistics

Standard deviation8.239370412
Coefficient of variation (CV)0.307064482
Kurtosis0.352243321
Mean26.83270419
Median Absolute Deviation (MAD)4.74
Skewness-0.01739703146
Sum28834584.92
Variance67.88722479
MonotonicityNot monotonic
2022-11-02T11:58:18.087318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.81050
 
0.1%
29.771027
 
0.1%
29.741023
 
0.1%
29.761021
 
0.1%
29.831017
 
0.1%
29.751009
 
0.1%
29.87999
 
0.1%
29.79989
 
0.1%
29.85974
 
0.1%
29.88973
 
0.1%
Other values (5381)1064524
99.1%
ValueCountFrequency (%)
-1.761
< 0.1%
-1.71
< 0.1%
-1.291
< 0.1%
-1.211
< 0.1%
-0.781
< 0.1%
-0.751
< 0.1%
-0.691
< 0.1%
-0.511
< 0.1%
-0.411
< 0.1%
-0.311
< 0.1%
ValueCountFrequency (%)
54.881
< 0.1%
54.861
< 0.1%
54.851
< 0.1%
54.771
< 0.1%
54.741
< 0.1%
54.642
< 0.1%
54.632
< 0.1%
54.612
< 0.1%
54.591
< 0.1%
54.582
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2166
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.604704617
Minimum0
Maximum27.77
Zeros64921
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:18.223681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.76
median2.15
Q33.85
95-th percentile6.81
Maximum27.77
Range27.77
Interquartile range (IQR)3.09

Descriptive statistics

Standard deviation2.409214602
Coefficient of variation (CV)0.924947338
Kurtosis14.46556707
Mean2.604704617
Median Absolute Deviation (MAD)1.51
Skewness2.360674215
Sum2799031.21
Variance5.804314999
MonotonicityNot monotonic
2022-11-02T11:58:18.509220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064921
 
6.0%
0.0116403
 
1.5%
0.029712
 
0.9%
0.037255
 
0.7%
0.045806
 
0.5%
0.054970
 
0.5%
0.064570
 
0.4%
0.074266
 
0.4%
0.084039
 
0.4%
0.093869
 
0.4%
Other values (2156)948795
88.3%
ValueCountFrequency (%)
064921
6.0%
0.0116403
 
1.5%
0.029712
 
0.9%
0.037255
 
0.7%
0.045806
 
0.5%
0.054970
 
0.5%
0.064570
 
0.4%
0.074266
 
0.4%
0.084039
 
0.4%
0.093869
 
0.4%
ValueCountFrequency (%)
27.771
< 0.1%
27.61
< 0.1%
27.361
< 0.1%
27.322
< 0.1%
27.251
< 0.1%
27.221
< 0.1%
27.171
< 0.1%
27.141
< 0.1%
27.111
< 0.1%
27.11
< 0.1%

a
Real number (ℝ≥0)

ZEROS

Distinct1546
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.804659438
Minimum0
Maximum27.5
Zeros60354
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:18.635632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.72
median1.55
Q32.6
95-th percentile4.48
Maximum27.5
Range27.5
Interquartile range (IQR)1.88

Descriptive statistics

Standard deviation1.443306065
Coefficient of variation (CV)0.7997664462
Kurtosis5.95139441
Mean1.804659438
Median Absolute Deviation (MAD)0.92
Skewness1.402756052
Sum1939297.86
Variance2.083132398
MonotonicityNot monotonic
2022-11-02T11:58:18.754567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060354
 
5.6%
0.0113098
 
1.2%
0.027404
 
0.7%
0.035734
 
0.5%
0.044624
 
0.4%
0.054004
 
0.4%
1.013452
 
0.3%
1.23434
 
0.3%
0.063416
 
0.3%
1.193366
 
0.3%
Other values (1536)965720
89.9%
ValueCountFrequency (%)
060354
5.6%
0.0113098
 
1.2%
0.027404
 
0.7%
0.035734
 
0.5%
0.044624
 
0.4%
0.054004
 
0.4%
0.063416
 
0.3%
0.073120
 
0.3%
0.082763
 
0.3%
0.092605
 
0.2%
ValueCountFrequency (%)
27.51
< 0.1%
26.971
< 0.1%
25.961
< 0.1%
24.441
< 0.1%
24.241
< 0.1%
24.191
< 0.1%
23.521
< 0.1%
23.411
< 0.1%
23.111
< 0.1%
23.091
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct549
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2636458293
Minimum0
Maximum8.08
Zeros65343
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:18.882029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.22
Q30.39
95-th percentile0.68
Maximum8.08
Range8.08
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.2584563439
Coefficient of variation (CV)0.9803164519
Kurtosis51.0160923
Mean0.2636458293
Median Absolute Deviation (MAD)0.15
Skewness4.271668359
Sum283315.39
Variance0.06679968171
MonotonicityNot monotonic
2022-11-02T11:58:19.007318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
065343
 
6.1%
0.0158033
 
5.4%
0.0233135
 
3.1%
0.0325486
 
2.4%
0.0422634
 
2.1%
0.0520891
 
1.9%
0.0619922
 
1.9%
0.1619651
 
1.8%
0.1819631
 
1.8%
0.1919500
 
1.8%
Other values (539)770380
71.7%
ValueCountFrequency (%)
065343
6.1%
0.0158033
5.4%
0.0233135
3.1%
0.0325486
 
2.4%
0.0422634
 
2.1%
0.0520891
 
1.9%
0.0619922
 
1.9%
0.0719442
 
1.8%
0.0819311
 
1.8%
0.0919103
 
1.8%
ValueCountFrequency (%)
8.081
< 0.1%
7.251
< 0.1%
6.631
< 0.1%
6.611
< 0.1%
6.481
< 0.1%
6.431
< 0.1%
6.41
< 0.1%
6.261
< 0.1%
6.231
< 0.1%
6.212
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.5%
Missing46722
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean182.0093449
Minimum0
Maximum360
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:19.138506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.76
Q191.38
median182.065
Q3270.95
95-th percentile330.83
Maximum360
Range360
Interquartile range (IQR)179.57

Descriptive statistics

Standard deviation99.07906599
Coefficient of variation (CV)0.5443625218
Kurtosis-1.366207262
Mean182.0093449
Median Absolute Deviation (MAD)89.775
Skewness-0.01051411614
Sum187084493.5
Variance9816.661318
MonotonicityNot monotonic
2022-11-02T11:58:19.261684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90173
 
< 0.1%
266.72110
 
< 0.1%
88.46106
 
< 0.1%
265.28105
 
< 0.1%
265.4105
 
< 0.1%
268.43105
 
< 0.1%
265.06105
 
< 0.1%
90.85102
 
< 0.1%
265.18102
 
< 0.1%
87.22102
 
< 0.1%
Other values (35991)1026769
95.5%
(Missing)46722
 
4.3%
ValueCountFrequency (%)
08
 
< 0.1%
0.0121
< 0.1%
0.0222
< 0.1%
0.0319
< 0.1%
0.0419
< 0.1%
0.0522
< 0.1%
0.0617
< 0.1%
0.0711
< 0.1%
0.0818
< 0.1%
0.0914
< 0.1%
ValueCountFrequency (%)
3607
 
< 0.1%
359.9918
< 0.1%
359.9817
< 0.1%
359.9716
< 0.1%
359.9616
< 0.1%
359.9512
< 0.1%
359.9427
< 0.1%
359.9312
< 0.1%
359.9225
< 0.1%
359.9113
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.5%
Missing46722
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean181.6884234
Minimum0
Maximum360
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2022-11-02T11:58:19.395650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.05
Q191.69
median181.97
Q3271.59
95-th percentile337.6
Maximum360
Range360
Interquartile range (IQR)179.9

Descriptive statistics

Standard deviation101.2341019
Coefficient of variation (CV)0.5571852075
Kurtosis-1.283374214
Mean181.6884234
Median Absolute Deviation (MAD)89.95
Skewness-0.0120161294
Sum186754623.4
Variance10248.34338
MonotonicityNot monotonic
2022-11-02T11:58:19.522303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.0272
 
< 0.1%
88.2572
 
< 0.1%
261.469
 
< 0.1%
260.469
 
< 0.1%
86.5869
 
< 0.1%
100.1569
 
< 0.1%
276.7268
 
< 0.1%
270.1568
 
< 0.1%
89.1168
 
< 0.1%
91.0768
 
< 0.1%
Other values (35991)1027192
95.6%
(Missing)46722
 
4.3%
ValueCountFrequency (%)
014
< 0.1%
0.0124
< 0.1%
0.0222
< 0.1%
0.0324
< 0.1%
0.0424
< 0.1%
0.0519
< 0.1%
0.0621
< 0.1%
0.0719
< 0.1%
0.0820
< 0.1%
0.0924
< 0.1%
ValueCountFrequency (%)
36010
 
< 0.1%
359.9930
< 0.1%
359.9821
< 0.1%
359.9730
< 0.1%
359.9631
< 0.1%
359.9522
< 0.1%
359.9428
< 0.1%
359.9334
< 0.1%
359.9225
< 0.1%
359.9131
< 0.1%

event
Categorical

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
None
992956 
ball_snap
 
25484
pass_forward
 
22448
autoevent_passforward
 
10534
autoevent_ballsnap
 
10189
Other values (17)
 
12995

Length

Max length25
Median length4
Mean length4.661016224
Min length3

Characters and Unicode

Total characters5008756
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None992956
92.4%
ball_snap25484
 
2.4%
pass_forward22448
 
2.1%
autoevent_passforward10534
 
1.0%
autoevent_ballsnap10189
 
0.9%
play_action6325
 
0.6%
run1541
 
0.1%
qb_sack1426
 
0.1%
pass_arrived1012
 
0.1%
man_in_motion598
 
0.1%
Other values (12)2093
 
0.2%

Length

2022-11-02T11:58:19.649329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none992956
92.4%
ball_snap25484
 
2.4%
pass_forward22448
 
2.1%
autoevent_passforward10534
 
1.0%
autoevent_ballsnap10189
 
0.9%
play_action6325
 
0.6%
run1541
 
0.1%
qb_sack1426
 
0.1%
pass_arrived1012
 
0.1%
man_in_motion598
 
0.1%
Other values (12)2093
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1060714
21.2%
o1055309
21.1%
e1038933
20.7%
N992956
19.8%
a176732
 
3.5%
s108169
 
2.2%
_80615
 
1.6%
p78476
 
1.6%
l78200
 
1.6%
r71047
 
1.4%
Other values (15)267605
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3935185
78.6%
Uppercase Letter992956
 
19.8%
Connector Punctuation80615
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1060714
27.0%
o1055309
26.8%
e1038933
26.4%
a176732
 
4.5%
s108169
 
2.7%
p78476
 
2.0%
l78200
 
2.0%
r71047
 
1.8%
t52532
 
1.3%
b37352
 
0.9%
Other values (13)177721
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N992956
100.0%
Connector Punctuation
ValueCountFrequency (%)
_80615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4928141
98.4%
Common80615
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1060714
21.5%
o1055309
21.4%
e1038933
21.1%
N992956
20.1%
a176732
 
3.6%
s108169
 
2.2%
p78476
 
1.6%
l78200
 
1.6%
r71047
 
1.4%
t52532
 
1.1%
Other values (14)215073
 
4.4%
Common
ValueCountFrequency (%)
_80615
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5008756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1060714
21.2%
o1055309
21.1%
e1038933
20.7%
N992956
19.8%
a176732
 
3.5%
s108169
 
2.2%
_80615
 
1.6%
p78476
 
1.6%
l78200
 
1.6%
r71047
 
1.4%
Other values (15)267605
 
5.3%

Interactions

2022-11-02T11:58:07.638142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:30.644427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:33.953028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:37.278397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:40.600384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:43.870493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:47.312006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:50.568098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:53.960779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:57.256591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:00.570172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:04.161556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:07.936686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:30.948296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:34.215888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:37.558737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:40.878900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:44.151100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:47.587119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:50.840076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:54.232255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:57.539827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:00.847243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:04.477760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:08.222167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:31.222069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:34.480359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:37.823904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:41.158434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:44.418974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:47.853997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:51.106138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:54.510846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:57.817593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:01.133734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:04.762668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:08.510599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:31.496073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:34.745807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:38.101963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:41.427269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:44.700507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:48.128248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:51.375763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:54.784781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:58.097630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:01.404497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:05.050608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:08.790925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:31.769238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:35.013205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:38.385744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:41.700350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:45.124653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:48.399636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:51.644834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:55.062954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:58.376526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:01.687364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:05.337477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:09.070786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:32.038864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:35.272826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:38.654530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:41.966710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:45.390236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:48.664716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:51.911559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:55.338127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:58.654308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:01.953218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:05.622749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:09.349279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:32.306381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:35.535843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:38.936996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:42.236282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:45.664048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:48.934197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:52.176835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:55.607243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:58.927978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:02.249605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:05.904733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:09.629136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:32.574812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:35.796031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:39.210227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:42.501640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:45.938215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:49.200487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:52.437935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:55.880133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:59.196552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:02.545228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:06.184216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:09.908436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:32.845366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:36.195556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:39.486682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:42.769037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:46.208418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:49.467410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:52.703576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:56.153406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:59.459296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:02.979671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:06.471912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:10.194736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:33.113195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:36.457406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:39.772333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:43.037402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:46.486905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:49.732271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:52.966457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:56.417368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:59.730477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:03.249238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:06.768773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:10.476231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:33.393530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:36.732667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:40.046155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:43.317896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:46.761564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:50.010102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:53.241771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:56.702948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:00.014115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:03.560278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:07.055912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:10.757336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:33.680637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:37.008851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:40.327176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:43.597049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:47.036311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:50.294967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:53.516797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:57:56.984353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:00.299167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:03.860338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:58:07.346445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T11:58:19.754121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T11:58:19.911128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T11:58:20.061328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T11:58:20.208770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T11:58:20.342894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T11:58:20.454199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T11:58:11.338908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T11:58:12.732151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T11:58:14.602269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T11:58:15.258548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
0202109300016938696.012021-10-01T00:29:42.80011.0JAXright54.6644.660.000.000.00107.61134.74None
1202109300016938696.022021-10-01T00:29:42.90011.0JAXright54.6644.660.000.000.00107.61130.79None
2202109300016938696.032021-10-01T00:29:43.00011.0JAXright54.6644.670.000.000.00107.61122.78None
3202109300016938696.042021-10-01T00:29:43.10011.0JAXright54.6644.660.000.000.00107.61134.78None
4202109300016938696.052021-10-01T00:29:43.20011.0JAXright54.6644.660.000.000.00107.61130.76None
5202109300016938696.062021-10-01T00:29:43.30011.0JAXright54.6644.660.000.000.00107.61141.35ball_snap
6202109300016938696.072021-10-01T00:29:43.40011.0JAXright54.6744.660.010.170.00108.20145.78None
7202109300016938696.082021-10-01T00:29:43.50011.0JAXright54.6744.660.080.720.01108.2082.47None
8202109300016938696.092021-10-01T00:29:43.60011.0JAXright54.6944.670.201.160.02108.9971.68None
9202109300016938696.0102021-10-01T00:29:43.70011.0JAXright54.7344.690.441.650.04108.9964.96None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
107459620211004004161NaN412021-10-05T03:45:54.700NaNfootballright17.2623.086.813.630.58NaNNaNNone
107459720211004004161NaN422021-10-05T03:45:54.800NaNfootballright17.7722.627.183.930.69NaNNaNNone
107459820211004004161NaN432021-10-05T03:45:54.900NaNfootballright18.3122.127.533.010.74NaNNaNNone
107459920211004004161NaN442021-10-05T03:45:55.000NaNfootballright18.8721.597.812.650.77NaNNaNNone
107460020211004004161NaN452021-10-05T03:45:55.100NaNfootballright19.4421.058.031.610.79NaNNaNpass_forward
107460120211004004161NaN462021-10-05T03:45:55.200NaNfootballright20.0320.498.140.630.81NaNNaNautoevent_passforward
107460220211004004161NaN472021-10-05T03:45:55.300NaNfootballright20.5420.018.730.490.70NaNNaNNone
107460320211004004161NaN482021-10-05T03:45:55.400NaNfootballright25.3316.5026.071.135.94NaNNaNNone
107460420211004004161NaN492021-10-05T03:45:55.500NaNfootballright27.4014.9225.892.452.60NaNNaNNone
107460520211004004161NaN502021-10-05T03:45:55.600NaNfootballright29.4413.3625.593.582.57NaNNaNNone